McDermott awarded FEED contract for PetroLogistics’ new PDH plant

Abstract

Due to the rapid exchange of information and large user base of social networking sites, these are focused for gathering the latest information or news from people over the world. Anyone having an internet connected device can share thoughts or update on real-time events. Social media helps reporters as well as common men in sharing useful information, but at the same time, it also leads to deliberate or accidental spread of rumors, i.e. pieces of information having uncertain truth at the time of posting. During social crisis, people access these platforms to get relevant information. In the rush of being early responders to a critical event users post the information even without checking its veracity and that further used by other users to fill-in their informational gap. So flagging out the unverified information can be useful in maintaining a distance from spreading the information that may end up being false. The openness of online social networking platforms (i.e. Twitter or Facebook), presence of machine learning and NLP (Natural Language Processing) based techniques give us a chance to inspect the conduct of people in posting rumorous information. In this work, we summarize and present the efforts and achievements so far to combat the spread of rumorous information. These efforts composed of analyzing the content of rumors, properties of users who share rumors and network structure that favor the spread of such information.